When You Don't Need MCP

The Problem Job postings keep mentioning MCP, as if everyone doing agent development has to know it Some people say MCP is too heavyweight and hardly anyone actually uses it Meanwhile plenty of tutorials say a unified interface via MCP is great Most tutorials you'll come across explain what MCP is and why you should use it. After all that explanation, it's still hard to get an intuitive feel for the trade-offs. So today I'll flip the question around: when do you not need MCP? That's a better way to build intuition about it. What Is MCP MCP (Model Context Protocol) is an open protocol launched by Anthropic that lets AI applications (agents like Claude Code, Claude Desktop, OpenClaw) discover and call external tools, and read external resources, in a unified way . That's the textbook definition. In practice, you can think of MCP as a kind of resource exposed to an agent. Before MCP existed, if you wanted an AI application to connect to services like Google Drive, GitHub, or Slack, every single AI application had to write its own integration code for every external service. MCP is essentially a "standard socket" defined for that connection. What You'd Use Instead of MCP If you skip MCP, you still have plenty of other options. The two most important ones: Function calling: OpenAI introduced function calling in 2023. It's actually simple — you pass a function signature to the LLM first. { "name": "get_weather", "description": "Get the current weather for a specified city", "input_schema": { ... "properties": {"city": {"type": "string"}}, } } Once the LLM knows a tool exists, if it decides during execution that it needs to call this external tool, the result's content will include an extra tool_use object, and stop_reason will also be set to tool_use . Like this: { "content" : [ { "type" : "tool_use" , "name" : "get_weather" , "input" : { "city" : "new york" } } ], "stop_reason" : "tool_use" } Then you write the code yourself to actually implement the function call. if re